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Record W2755598992 · doi:10.1177/0883073817726681

Prognostic Value of Brain Magnetic Resonance Imaging in Neonatal Hypoxic-Ischemic Encephalopathy: A Meta-analysis

2017· review· en· W2755598992 on OpenAlexaff
Iván Sánchez Fernández, Jorge Leon Morales-Quezada, Samuel Law, Paggie Kim

Bibliographic record

VenueJournal of Child Neurology · 2017
Typereview
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersNational Center for Complementary and Integrative Health
KeywordsOdds ratioMedicineConfidence intervalHypoxic Ischemic EncephalopathyEncephalopathyMagnetic resonance imagingHypothermiaNeonatal encephalopathyInternal medicineCardiologyAnesthesiaRadiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To quantify the prognostic value of neonatal brain magnetic resonance imaging (MRI) in neonatal hypoxic-ischemic encephalopathy. METHODS: Meta-analysis of studies with ≥35-week neonates with hypoxic-ischemic encephalopathy who underwent brain MRI within age 4 weeks and had neurodevelopmental follow-up for at least 12 months. RESULTS: An abnormal neonatal brain MRI was more frequent among patients with unfavorable neurodevelopmental outcome: odds ratio = 18.2 (95% confidence interval: 9.4-34.9), P <.0001. The prognostic value of neonatal brain MRI in moderate hypoxic-ischemic encephalopathy had an odds ratio of 17.7 (95% confidence interval: 5.3-59.3) and in severe hypoxic-ischemic encephalopathy, the odds ratio was 125.0 (95% confidence interval: 2.0-7917.1). Therapeutic hypothermia did not change the prognostic value of neonatal brain MRI (odds ratio for hypothermia, 14.0 [95% confidence interval: 3.1-63.6], vs no hypothermia, 18.1 [95% confidence interval: 10.0-33.1], P = .7525). CONCLUSION: Neonatal brain MRI provides prognostic information on outcome beyond early infancy in hypoxic-ischemic encephalopathy and therapeutic hypothermia does not change its prognostic value.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.331
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations55
Published2017
Admission routes1
Has abstractyes

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